A Model for Generating Real Stream Data in Simulated Edge Computing Environments

Abstract

Due to the limitations of device resources and the dynamic nature of data in edge computing environments, developers face significant challenges in acquiring, processing, and analyzing high-quality streaming data during development and testing. This paper examines the characteristics of streaming data in edge computing environments, summarizes its volatility and changing trends in the real world, and deduces potential periodic patterns inherent in such data. A model for generating realistic streaming data in simulated edge computing environments is proposed to address these challenges. The model first preprocesses the original streaming data to extract time-related information (timestamps or precise time). Then, the NSA algorithm is applied to reduce the data volume, followed by the use of the PSD parallel algorithm to transmit the sampled data. Experimental comparisons demonstrate that the model effectively preserves the volatility and trend characteristics of the original streaming data, providing a viable solution for the difficulties developers face in obtaining and processing high-quality streaming data in edge computing environments during development and testing.